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<div class="title">multi_ransac.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Copyright (c) 2009, Willow Garage, Inc.</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  All rights reserved.</span></div>
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<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
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<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#ifndef PCL_CUDA_SAMPLE_CONSENSUS_RANSAC_H_</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#define PCL_CUDA_SAMPLE_CONSENSUS_RANSAC_H_</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#include &lt;pcl/cuda/sample_consensus/sac.h&gt;</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &lt;pcl/cuda/sample_consensus/sac_model.h&gt;</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160; </div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="keyword">namespace </span>pcl</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;{</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;  <span class="keyword">namespace </span>cuda</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;  {</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <span class="keyword">template</span> &lt;<span class="keyword">template</span> &lt;<span class="keyword">typename</span>&gt; <span class="keyword">class </span>Storage&gt;</div>
<div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html">   56</a></span>&#160;    <span class="keyword">class </span><a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html">MultiRandomSampleConsensus</a> : <span class="keyword">public</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus</a>&lt;Storage&gt;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    {</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::max_iterations_</a>;</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::threshold_</a>;</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::iterations_</a>;</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::sac_model_</a>;</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::model_</a>;</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::model_coefficients_</a>;</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::inliers_</a>;</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::inliers_stencil_</a>;</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus&lt;Storage&gt;::probability_</a>;</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160; </div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> SampleConsensusModel&lt;Storage&gt;::Ptr SampleConsensusModelPtr;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> SampleConsensusModel&lt;Storage&gt;::Coefficients Coefficients;</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> SampleConsensusModel&lt;Storage&gt;::Hypotheses Hypotheses;</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160; </div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> SampleConsensusModel&lt;Storage&gt;::Indices Indices;</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> SampleConsensusModel&lt;Storage&gt;::IndicesPtr IndicesPtr;</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> SampleConsensusModel&lt;Storage&gt;::IndicesConstPtr IndicesConstPtr;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;      <span class="keyword">public</span>:</div>
<div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#af3a795dc44e988143c2bedb97f864938">   80</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#af3a795dc44e988143c2bedb97f864938">MultiRandomSampleConsensus</a> (<span class="keyword">const</span> SampleConsensusModelPtr &amp;model) : </div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;          <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus</a>&lt;Storage&gt; (model),</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;          min_coverage_percent_ (0.9),</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;          max_batches_ (5),</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;          iterations_per_batch_ (1000)</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;        {</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;          <span class="comment">// Maximum number of trials before we give up.</span></div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;          <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html#a56f971cd1025e8bcaa1c6da13080dca2">max_iterations_</a> = 10000;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        }</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
<div class="line"><a name="l00094"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a2d30c49d773e4ac88bf50d5019edd09e">   94</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a2d30c49d773e4ac88bf50d5019edd09e">MultiRandomSampleConsensus</a> (<span class="keyword">const</span> SampleConsensusModelPtr &amp;model, <span class="keywordtype">double</span> threshold) : </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;          <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html">SampleConsensus</a>&lt;Storage&gt; (model, threshold)</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        {</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;          <span class="comment">// Maximum number of trials before we give up.</span></div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;          <a class="code" href="classpcl_1_1cuda_1_1_sample_consensus.html#a56f971cd1025e8bcaa1c6da13080dca2">max_iterations_</a> = 10000;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        }</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160; </div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        <span class="keywordtype">bool</span> </div>
<div class="line"><a name="l00106"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ae85ed7f021732575132f9a4b30a73eb5">  106</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ae85ed7f021732575132f9a4b30a73eb5">computeModel</a> (<span class="keywordtype">int</span> debug_verbosity_level = 0);</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;        <span class="keywordtype">void</span></div>
<div class="line"><a name="l00112"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a6373987e0ed5079557c4f2c106f0a070">  112</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a6373987e0ed5079557c4f2c106f0a070">setMinimumCoverage</a> (<span class="keywordtype">float</span> percent)</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;        {</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;          min_coverage_percent_ = percent;</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;        }</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;          </div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        <span class="keywordtype">void</span></div>
<div class="line"><a name="l00123"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a1e82feb9d732943a581a75a6737c184d">  123</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a1e82feb9d732943a581a75a6737c184d">setMaximumBatches</a> (<span class="keywordtype">int</span> max_batches)</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        {</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;          max_batches_ = max_batches_;</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;        }</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160; </div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        <span class="keywordtype">void</span></div>
<div class="line"><a name="l00134"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a79a1d7b8f189cbdc7021d9fd6aec0280">  134</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a79a1d7b8f189cbdc7021d9fd6aec0280">setIerationsPerBatch</a>(<span class="keywordtype">int</span> iterations_per_batch)</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;        {</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;          iterations_per_batch_ = iterations_per_batch;</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        }</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;        <span class="keyword">inline</span> std::vector&lt;IndicesPtr&gt;</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;        getAllInliers () { <span class="keywordflow">return</span> all_inliers_; }</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160; </div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;        <span class="keyword">inline</span> std::vector&lt;int&gt;</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        getAllInlierCounts () { <span class="keywordflow">return</span> all_inlier_counts_; }</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160; </div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;        <span class="keyword">inline</span> std::vector&lt;float4&gt;</div>
<div class="line"><a name="l00148"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab55b29e094fb6a4eb34c50b0af5c892e">  148</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab55b29e094fb6a4eb34c50b0af5c892e">getAllModelCoefficients</a> () </div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        { </div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;          <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a167354539f4e650dd2f3c5c60bbf505f">all_model_coefficients_</a>; </div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;        }</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;        <span class="keyword">inline</span> std::vector&lt;float3&gt;</div>
<div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#adb8c9c810673d6c581b17963ca015d2c">  156</a></span>&#160;        <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#adb8c9c810673d6c581b17963ca015d2c">getAllModelCentroids</a> () </div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;        { </div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;          <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab45b595401f5795286dd1a2e2d4358c9">all_model_centroids_</a>; </div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        }</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;      <span class="keyword">private</span>:</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        <span class="keywordtype">float</span> min_coverage_percent_;</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_batches_;</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> iterations_per_batch_;</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160; </div>
<div class="line"><a name="l00167"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab45b595401f5795286dd1a2e2d4358c9">  167</a></span>&#160;        std::vector&lt;float3&gt; <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab45b595401f5795286dd1a2e2d4358c9">all_model_centroids_</a>;</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
<div class="line"><a name="l00170"></a><span class="lineno"><a class="line" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a167354539f4e650dd2f3c5c60bbf505f">  170</a></span>&#160;        std::vector&lt;float4&gt; <a class="code" href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a167354539f4e650dd2f3c5c60bbf505f">all_model_coefficients_</a>;</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160; </div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;        std::vector&lt;IndicesPtr&gt; all_inliers_;</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;        std::vector&lt;int&gt; all_inlier_counts_;</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    };</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160; </div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;  } <span class="comment">// namespace</span></div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;} <span class="comment">// namespace</span></div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160; </div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">//#ifndef PCL_CUDA_SAMPLE_CONSENSUS_RANSAC_H_</span></div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html">pcl::cuda::MultiRandomSampleConsensus</a></div><div class="ttdoc">RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm,...</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:57</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_a167354539f4e650dd2f3c5c60bbf505f"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a167354539f4e650dd2f3c5c60bbf505f">pcl::cuda::MultiRandomSampleConsensus::all_model_coefficients_</a></div><div class="ttdeci">std::vector&lt; float4 &gt; all_model_coefficients_</div><div class="ttdoc">The vector of coefficients of our models computed directly from the models found.</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:170</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_a1e82feb9d732943a581a75a6737c184d"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a1e82feb9d732943a581a75a6737c184d">pcl::cuda::MultiRandomSampleConsensus::setMaximumBatches</a></div><div class="ttdeci">void setMaximumBatches(int max_batches)</div><div class="ttdoc">Sets the maximum number of batches that should be processed. Every Batch computes up to iterations_pe...</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:123</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_a2d30c49d773e4ac88bf50d5019edd09e"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a2d30c49d773e4ac88bf50d5019edd09e">pcl::cuda::MultiRandomSampleConsensus::MultiRandomSampleConsensus</a></div><div class="ttdeci">MultiRandomSampleConsensus(const SampleConsensusModelPtr &amp;model, double threshold)</div><div class="ttdoc">RANSAC (RAndom SAmple Consensus) main constructor</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:94</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_a6373987e0ed5079557c4f2c106f0a070"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a6373987e0ed5079557c4f2c106f0a070">pcl::cuda::MultiRandomSampleConsensus::setMinimumCoverage</a></div><div class="ttdeci">void setMinimumCoverage(float percent)</div><div class="ttdoc">how much (in percent) of the point cloud should be covered? If it is not possible to find enough plan...</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:112</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_a79a1d7b8f189cbdc7021d9fd6aec0280"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#a79a1d7b8f189cbdc7021d9fd6aec0280">pcl::cuda::MultiRandomSampleConsensus::setIerationsPerBatch</a></div><div class="ttdeci">void setIerationsPerBatch(int iterations_per_batch)</div><div class="ttdoc">Sets the maximum number of batches that should be processed. Every Batch computes up to max_iteration...</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:134</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_ab45b595401f5795286dd1a2e2d4358c9"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab45b595401f5795286dd1a2e2d4358c9">pcl::cuda::MultiRandomSampleConsensus::all_model_centroids_</a></div><div class="ttdeci">std::vector&lt; float3 &gt; all_model_centroids_</div><div class="ttdoc">The vector of the centroids of our models computed directly from the models found.</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:167</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_ab55b29e094fb6a4eb34c50b0af5c892e"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ab55b29e094fb6a4eb34c50b0af5c892e">pcl::cuda::MultiRandomSampleConsensus::getAllModelCoefficients</a></div><div class="ttdeci">std::vector&lt; float4 &gt; getAllModelCoefficients()</div><div class="ttdoc">Return the model coefficients of the best model found so far.</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:148</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_adb8c9c810673d6c581b17963ca015d2c"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#adb8c9c810673d6c581b17963ca015d2c">pcl::cuda::MultiRandomSampleConsensus::getAllModelCentroids</a></div><div class="ttdeci">std::vector&lt; float3 &gt; getAllModelCentroids()</div><div class="ttdoc">Return the model coefficients of the best model found so far.</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:156</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_ae85ed7f021732575132f9a4b30a73eb5"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#ae85ed7f021732575132f9a4b30a73eb5">pcl::cuda::MultiRandomSampleConsensus::computeModel</a></div><div class="ttdeci">bool computeModel(int debug_verbosity_level=0)</div><div class="ttdoc">Compute the actual model and find the inliers</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_multi_random_sample_consensus_html_af3a795dc44e988143c2bedb97f864938"><div class="ttname"><a href="classpcl_1_1cuda_1_1_multi_random_sample_consensus.html#af3a795dc44e988143c2bedb97f864938">pcl::cuda::MultiRandomSampleConsensus::MultiRandomSampleConsensus</a></div><div class="ttdeci">MultiRandomSampleConsensus(const SampleConsensusModelPtr &amp;model)</div><div class="ttdoc">RANSAC (RAndom SAmple Consensus) main constructor</div><div class="ttdef"><b>Definition:</b> multi_ransac.h:80</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_sample_consensus_html"><div class="ttname"><a href="classpcl_1_1cuda_1_1_sample_consensus.html">pcl::cuda::SampleConsensus</a></div><div class="ttdef"><b>Definition:</b> sac.h:52</div></div>
<div class="ttc" id="aclasspcl_1_1cuda_1_1_sample_consensus_html_a56f971cd1025e8bcaa1c6da13080dca2"><div class="ttname"><a href="classpcl_1_1cuda_1_1_sample_consensus.html#a56f971cd1025e8bcaa1c6da13080dca2">pcl::cuda::SampleConsensus::max_iterations_</a></div><div class="ttdeci">int max_iterations_</div><div class="ttdoc">Maximum number of iterations before giving up.</div><div class="ttdef"><b>Definition:</b> sac.h:196</div></div>
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